The standards for functional safety (e.g. IEC 61508, ISO 13849 and IEC 62061) opened up a new era in the design of machinery, in which the safety of the control system is evaluated according to its reliability. In order to prove the safety of their machines, designers now need different parameters, e.g. MTTFd or B10d. Based on a ISO/IEC survey from 2012, the availability of those parameters is the main problem in applying functional safety standards. But what exactly do these parameters mean? How can these parameters be determined for different control technologies?

This presentation offers an explanation of the main methods for the determination of the reliability parameters for functional safety for hydraulic components.

Although reliability is often confused with statistical parameters, it is much more a product characteristic that is to be designed systematically according to the definition in EN 13306: \"Ability of an item to perform a required function (with defined limits) under given conditions (loads) for a given time interval (service time). The term \'reliability\' is also used as a variable for the degree of reliability and can also be defined as a probability (e.g. number of failures during the service time).\"

Component manufacturers provide a variety of reliability parameters (e.g. MTTFd or B10), depending on the technology. These are statistically expected values that depend heavily on the method employed and the operating conditions. The methods for determining the reliability parameters for functional safety can essentially be broken down into three principle types: calculation, testing and evaluation of field data. This presentation presents all three approaches; their difficulties, prerequisites and neces-sary assumptions.

The calculation of reliability data of hydraulic components is mainly based on the standardized MTTFd values from ISO 13849. The presentation includes a qualified method to confirm the necessary basic and well-tried principles and how to calculate the combined values of complex hydraulic components.

In terms of testing, this contribution presents different testing strategies to determine B10 and therefore B10d values. Necessary input for such a lifetime testing are work conditions like pressure, flow, load cycles, which have a major impact on the deter-mined values and which need to be standardized in typical application loads to come up with comparable results. Beside the input values, further definitions are required for the evaluation, e.g. confidence intervals and ratio of dangerous failure.

The evaluation of field data is the last presented approach to determine reliability data. As before, the authors focus on different assumptions to evaluate available data and present problems and approaches to deal with the accuracy of field data.

Finally, the presentation summarizes the main benefits and discrepancies of the three presented methods.

The entire presentation is guided by an example of an electro-hydraulic control valve.

Lumped parameter system simulations are a commonly used tool in hydraulic system design and dimensioning. Most models are volume based and therefore inappropriate for many applications. Especially in closed loop systems, long term simulations and in systems where the consideration of entrained air is necessary, models based on the physically correct mass balance are essential to provide accurate simulation results. The basis of lumped parameter simulations is the capacity as it is the key component to calculate the pressure build-up.

State of the art equations to model this pressure build-up are volume based and consider only the pressure build-up in a liquid phase volume. A possibly existing gaseous phase is respected by an effective bulk-modulus. There also exist a few models that use mass balances and mass flows to calculate the pressure build-up in capacities but do not include the time dependency of the phase change between two existing phases.

In this paper a new model is presented that allows the calculation of the pressure build-up and decrease in a multi-phase capacity with the overall goal to increase lumped parameter simulations accuracy. Therefore the model considers different compositions of the fluid at the start of simulation. Phase changing effects like the solution and release of air are included taking the speed of these effects into account. The necessary data were experimentally determined for a commonly used hydraulic mineral oil and an ester based oil. The model allows the calculation of the pressure build-up and reduction due to volume changes, changes in the total mass of the considered volume as well as time dependent phase changes of the elements.

To validate the model measurements are performed allowing a precise recording of the pressure build-up and reduction in a rigid test chamber. This special remodelled test-rig provides the possibility to vary separately the volume, the liquids phase mass content, and the gaseous phase mass content. Additionally the test chamber is leak-proof so that time variant effects can be considered in the measurements.

In hydraulic systems air in oil appear as undissolved air (free air) or dissolved air. The solubility of air in oil depends on oil type, temperature und system pressure. Air bubbles are formed when the pressure falls below the solubility limit. This process is faster than the opposite process, the dissolution of air in oil below the solubility limit. Free air can lead to cavitation and micro-diesel effects which may damage the hydraulic equipment and lead to premature oil ageing. Thus the design target for the system is to get as less air bubbles as possible and that existent free air will be separated from the oil. The higher the retention time of oil in hydraulic tanks is the more air is segregated. But due to packaging reasons the available space for hydraulic tanks in agricultural or construction machines is decreasing. Tank design in terms of space and geometry is influenced by a more compact machine design and by requirements of exhaust after-treatment systems. So the objective of inner tank design is to reach good air release properties despite of short retention time.

In previous projects at the Institute of Mobile Machines and Commercial Vehicles (IMN) criteria for the characterization of foaming behaviour were developed. Furthermore the effects of air in oil on the system dynamics and on the efficiency of hydrostatic transmissions with dry case motors were investigated. Since the operation of dry case motors causes an increase of air content in the oil appropriate tank designs were evaluated. A test bench for a transmission with different hydraulic test tanks was built allowing to measure pressures and temperatures as operation parameters and to visually control the oil at different positions in the hydraulic system and the tank. As a result of the evaluation it can be stated that the air separation ability of hydraulic tanks can be improved by employing adapted mechanical measures. However, oil behaviour is depending on multiple parameters such as temperatures, flow rates and others. For this reason tank design cannot be developed efficiently only with this empiric approach.

At the IMN computational fluid dynamics (CFD) is chosen as a tool to evaluate appropriate tank designs regarding their air separation properties. Project objective is to find the best tank alternatives. Therefore two-phase-simulation approaches are under development. The simulation of those oil-air combinations in hydraulic tanks will be validated by test bench investigations.

In the proposed paper at first existing approaches for the improvements of air separation properties will be outlined and discussed. Following possible definitions for oil quality and oil behaviour in hydraulic tanks regarding their appropriateness for hydraulic systems of mobile machines will be discussed. The relevant input and output parameters for simulation will be derived. Then approaches for the simulations that have to be considered for this research project will be presented and discussed.
In the last part of the paper the test bench concept will be presented. Duty cycles of the hydraulic tanks in the real scale of a mobile machine will be applied on this test bench. The tanks therefore have to be furnished with flow rates, temperatures and oil qualities similar to those of the real machine. For the validation of the simulation outcome of the test runs will be the flow properties, temperatures and parameters that determine the oil quality.

Die Komplexität der Antriebs- und Steuerungstechnik mobiler Arbeitsmaschinen steigt in den letzten Jahren aufgrund des wachsenden Anteils von Elektronik. Die Einbindung elektronischer Systeme bietet ein erhebliches Potenzial zur Steigerung der Leistungsfähigkeit und Effizienz. Die Software wird dabei zur funktionsbestimmenden Schlüsselkomponente.
Die Funktionserprobung der Steuerung erfordert auch unter dem Aspekt der Maschinensicherheit neue und effiziente Methoden. Bisher erfolgte der funktionale Test erst an der realen Maschine. Diese Vorgehensweise ist nicht nur ressourcenintensiv, sondern auch sehr risikoreich. Hardware-in-the-Loop-(HiL)-Lösungen stellen eine Möglichkeit der frühzeitigen Prüfung dar. Dabei bildet eine virtuelle Umgebung das reale Verhalten der zu entwickelnden Maschine ab. Kritische Fehlerszenarien lassen sich so umfangreich und gefahrlos erproben. In der Automobilbranche ist der Einsatz bereits länger gängige Praxis.
Die HiL-Methode bedingt die Echtzeitfähigkeit der Simulation. Existierende Modelle aus dem Systementwurf werden diesem Anspruch oft nicht gerecht. Die Umsetzung der Echtzeitanforderungen ist sehr zeit- und kostenintensiv. Dazu sind Erfahrung und Systemkenntnis erforderlich. Es ist meist notwendig, das Modell in seiner Komplexität zu reduzieren, ohne dabei verhaltensrelevante Eigenschaften zu vernachlässigen. Der Aufwand verhindert bisher die Verbreitung der Methodik vor allem bei klein- und mittelständischen Herstellern mobiler Arbeitsmaschinen.
Der Begriff Echtzeit beinhaltet den Anspruch nach Recht- und Gleichzeitigkeit der Systemreaktion auf einen externen Prozess. Innerhalb des Steuergerätetakts müssen alle externen Daten korrekt verarbeitet und alle Ausgangssignale bereitgestellt werden. Bezogen auf die Simulation muss die Rechenzeit zur Auswertung des Modells stets kleiner als die zugehörige Zeitschrittweite sein. Zusätzlich ist die numerische Stabilität des Integrationsverfahrens eine begrenzende Eigenschaft. Je höher die Eigenwerte des Systems, umso kleiner muss die Schrittweite gewählt werden. Steife Systeme führen so zu sehr kleinen Schrittweiten. Dies schränkt den berechenbaren Modellumfang erheblich ein. Zudem ist die Auswertung algebraischer Bedingungen oder die genaue Identifikation von Unstetigkeitsstellen nur eingeschränkt zulässig.
Die Simulation fluidtechnischer Systeme in Echtzeit wurde bisher in der Literatur nur sehr rudimentär behandelt. Dabei führt die Modellbeschreibung im Kontext der Echtzeitsimulation oft zu Schwierigkeiten. Die Modelle weisen ein stark nichtlineares Verhalten auf. Bestimmte Modellansätze führen zu impliziten Ausdrücken. Charakteristische Elemente wie Sperrventile und starre Anschläge verursachen Unstetigkeiten. Kleine Volumina in Verbindung mit großen Volumenströmen führen zu steifen Systemen.
Der Beitrag beschreibt am Beispiel der Arbeitsausrüstung einer mobilen Arbeitsmaschine die Vorgehensweise bei der Generierung eines Echtzeitmodells. Grundlage ist ein validiertes nicht echtzeitfähiges Simulationsmodell aus dem Systementwurf. Die Anforderungen an die Echtzeitmodellierung bilden dem Ausgangspunkt der Erläuterungen. Das Paper zeigt den Einsatz von Analysewerkzeugen der Simulationsumgebung zur Identifikation kritischer Elemente. Im Fokus des Artikels steht jedoch vor allem die echtzeitfähige Modellbildung des hydraulisch-mechanischen Antriebs. Hierzu wird die vorhandene fluidtechnische Modellbibliothek der Simulationsumgebung ergänzt. Rechenzeitintensive Beschreibungen, Unstetigkeiten sowie iterative Auswertungen innerhalb der Elemente werden durch günstigere Formulierungen ersetzt. Steife Verbindungen außerhalb des verhaltensrelevanten Bereichs werden in quasistatische Zusammenhänge umgeformt. Abschließend wird anhand des Vergleichs mit Messdaten eines Applikationsprüfstandes die erreichbare Genauigkeit bewertet. Die aufgezeigten Maßnahmen und Anpassungen sind grundsätzlich auch auf andere Antriebslösungen und Simulationsumgebungen übertragbar.